Publication
An Economic Model for Self-tuned Cloud Caching
Publications associées (32)
Using Cloud Functions as Accelerator for Elastic Data Analytics
Anastasia Ailamaki, Haoqiong Bian, Tiannan Sha
Cloud function (CF) services, such as AWS Lambda, have been applied as the new computing infrastructure in implementing analytical query engines. For bursty and sparse workloads, CF-based query engine is more elastic than the traditional query engines runn ...
ACM2023Time- and Space-Efficient Spatial Data Analytics
Advances in data acquisition technologies and supercomputing for large-scale simulations have led to an exponential growth in the volume of spatial data. This growth is accompanied by an increase in data complexity, such as spatial density, but also by mor ...
EPFL2019Efficient Bundled Spatial Range Queries
Anastasia Ailamaki, Darius Sidlauskas, Thomas Heinis, Farhan Tauheed, Eleni Tzirita Zacharatou
Efficiently querying multiple spatial data sets is a growing challenge for scientists. Astronomers query data sets that contain different types of stars (e.g., dwarfs, giants, stragglers) while neuroscientists query different data sets that model different ...
ASSOC COMPUTING MACHINERY2019Timely and cost-efficient data exploration through adaptive tuning
Modern applications accumulate data at an exponentially increasing rate and traditional database systems struggle to keep up.
Decision support systems used in industry, rely heavily on data analysis, and require real-time responses irrespective of data siz ...
EPFL2019Just-in-time Analytics Over Heterogeneous Data and Hardware
Industry and academia are continuously becoming more data-driven and data-intensive, relying on the analysis of a wide variety of datasets to gain insights. At the same time, data variety increases continuously across multiple axes. First, data comes in mu ...
EPFL2017Distributed Time Series Analytics
In recent years time series data has become ubiquitous thanks to affordable sensors and advances in embedded technology. Large amount of time-series data are continuously produced in a wide spectrum of applications, such as sensor networks, medical monitor ...
EPFL2017Toward timely, predictable and cost-effective data analytics
Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely, predictable and cost-effective analytical processing of such large data sets in order to ext ...
EPFL2016From Massive Parallelization to Quantum Computing: Seven Novel Approaches to Query Optimization
The goal of query optimization is to map a declarative query (describing data to generate) to a query plan (describing how to generate the data) with optimal execution cost. Query optimization is required to support declarative query interfaces. It is a co ...
EPFL2016Runtime Prediction for Scale-Out Data Analytics
Many analytics applications generate mixed workloads, i.e., workloads comprised of analytical tasks with different processing characteristics including data pre-processing, SQL, and iterative machine learning algorithms. Examples of such mixed workloads ca ...
EPFL2015Adaptive Query Processing on Raw Data Files
Nowadays, business and scientific applications accumulate data at an increasing pace. This growth of information has already started to outgrow the capabilities of database management systems (DBMS). In a typical DBMS usage scenario, the user should define ...
EPFL2015